Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback. It includes a neurostimulator to provide direct neural feedback based on sensory information. The system was validated using real-time tasks characterization, power consumption evaluation, and myoelectric pattern recognition performance. Functionality was proven in a first pilot patient from whom results of daily usage were obtained. The system was designed to be reliably used in activities of daily living, as well as a research platform to monitor prosthesis usage and training, machine-learning-based control algorithms, and neural stimulation paradigms.
Background: Vagus nerve stimulation is a treatment for refractory epilepsy. The vagus nerve carries parasympathetic information and innervates multiple organs. As seizures are commonly associated with autonomic manifestations, we believe that biomarkers for diseases affecting autonomic functions such as epilepsy can be found in vagus nerve signals. New method: We present a method to record vagus nerve electroneurogram (VENG) and detect in the VENG single unit activity in anesthetized rats during Pentylenetetrazol induced seizures using a true tripolar cuff electrode.Results: The VENG consisted of high amplitude bursts and lower amplitude bursts synchronous to respiration and heartbeat respectively. The average spikes exhibited a triphasic shape with duration below 1.5ms and root mean square amplitude varied between 5.5 +/-0.2 μV and 11.4 +/-3.1 μV depending on the type of recording. An increase of the contact distance resulted in a signal amplitude increase. Application of Lidocaine led to a total disappearance of the recorded spontaneous spiking of the nerve. Comparison with existing methods: True tripolar cuff electrodes exhibited a better performance in terms of artefact rejection, stability and reproducibility of the signal compared to commonly used hook electrodes which is of special interest in seizures where important motion and EMG artifacts are expected. Conclusion:We present a new method to record single unit activity of the vagus nerve during acute chemically induced seizures in rats and verified the neural origin of the recorded signals. This recording method might be a powerful tool to develop seizure biomarkers based on VENG.Recently, a new model of VNS has been commercialized (AspireSR), which was designed to exploit ictal tachycardia using a patented cardiac-based seizure-detecting algorithm. The device triggers VNS on the basis of tachycardia. The performance of this automated seizure detection was assessed in a prospective observational multi-site study (Boon et al., 2015). Despite the rather accurate system, the expected additional or potential benefit for patients is still a matter of debate. A possible explanation is that a substantial number of patients do not have ictal tachycardia while this is a basic requirement for this "closed loop" system. However, the abortive effect of on demand VNS, prior or soon after seizure onset is being confirmed by several human and
Objective. Finite element modelling has been widely used to understand the effect of stimulation on the nerve fibres. Yet the literature on analysis of spontaneous nerve activity is much scarcer. In this study, we introduce a method based on a finite element model, to analyse spontaneous nerve activity with a typical bipolar electrode recording setup, enabling the identification of spontaneously active fibres. We applied our method to the vagus nerve, which plays a key role in refractory epilepsy. Approach. We developed a 3D model including dynamic action potential propagation, based on the vagus nerve geometry. The impact of key recording parametersinter-electrode distance and temperatureand uncontrolled parametersfibre size and position in the nerveon the ability to discriminate active fibres were quantified. A specific algorithm was implemented to detect and classify action potentials from recordings, and tested on six rat in-vivo vagus nerve recordings. Main results. Fibre diameters can be discriminated if they are below 3 µm and 7 µm, respectively for inter-electrode distances of 2 mm and 4 mm. The impact of the position of the fibre inside the nerve on fibre diameter discrimination is limited. The range of active fibres identified by modelling in the vagus nerve of rats is in agreement with ranges found at histology. Significance. The nerve fibre diameter, directly proportional to the action potential propagation velocity, is related to a specific physiological function. Estimating the source fibre diameter is thus essential to interpret neural recordings. Among many possible applications, the present method was developed in the context of a project to improve vagus nerve stimulation therapy for epilepsy.
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